Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.
4 Pages
tutorial9
Course: ECON 2206, Fall 2011School: UNSW
Rating:
Word Count: 1008
Document Preview
Week9TutorialExercises ReviewQuestions(thesemayormaynotbediscussedintutorialclasses) Whatisheteroskedasticityinaregressionmodel? Whenhomoskedasticity(MLR5)failsandthevarianceofthedisturbance(u)changesacross observationindex(i),wesaythatheteroskedasticityispresent. Inthepresenceofheteroskedasticity,arethetstatandFstatfromtheusualOLSstillvalid?...
Unformatted Document Excerpt
Coursehero >> Australia >> UNSW >> ECON 2206Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Week9TutorialExercises
ReviewQuestions(thesemayormaynotbediscussedintutorialclasses)
Whatisheteroskedasticityinaregressionmodel?
Whenhomoskedasticity(MLR5)failsandthevarianceofthedisturbance(u)changesacross
observationindex(i),wesaythatheteroskedasticityispresent.
Inthepresenceofheteroskedasticity,arethetstatandFstatfromtheusualOLSstillvalid?
Why?ArethereanyotherproblemswiththeOLSunderheteroskedasticity?
TheusualtstatandFstatarenotvalidbecausetheusualOLSstandarderrorsareincorrect
underheteroskedasticity.FurthertheOLSestimatorwillnolongerbe(asymptotically)efficient
andtherearebetterestimators.
Whataretheheteroskedasticityrobuststandarderrors?HowdoyouusetheminSTATA?
Thesearethecorrectedstandarderrorsthattakeintoaccountthepossiblepresenceof
heteroskedasticity.ThetstatandFstatcomputedusingtheheteroskedasticityrobuststandard
errorsarevalidteststatistics.InSTATA,theseareeasilyobtainedbyusingtheoptionrobust
withtheregresscommand,e.g.,regress lwage educ, robust;
Howdoyoudetectifthereisheteroskedasticity?
TheBreuschPagantestortheWhitetestcanbeusedtodetectthepresenceof
heteroskedasticity.SeeSection8.3fordetails.
Ifheteroskedasticityispresentinaknownform,howwouldyouestimatethemodel?
Inthiscase,theWLSestimatorsshouldbeused,whichismoreefficientthantheOLSestimators.
SeeSection8.4fordetails.
Ifheteroskedasticityispresentinanunknownform,howwouldyouestimatethemodel?
Ifthereisstrongevidenceforheteroskedasticity,theFGLSestimatorsshouldbeused,whichis
basedonanexponentialfunctionalform.SeeSection8.4fordetails.
WhatarethestepsintheFGLSestimation?
YoushouldsummarisefromSection8.4.
HowwouldyouhandletheheteroskedasticityoftheLPM?
TheheteroskedasticityfunctionalformisknownfortheLPM.HencetheWLScanbeusedin
principle.However,becausetheLPMcanproducepredictedprobabilitiesthatareoutsidethe
interval(0,1),theknownfunctionalformp(x)[1p(x)]maynotbeusefulfortheWLS.Inthatcase,
itmaybenecessarytouseFGLSfortheLPM.
ProblemSet(thesewillbediscussedintutorialclasses)
Q1.Wooldridge8.1
Parts(ii)and(iii).ThehomoskedasticityassumptionplayednoroleinChapter5inshowingthat
OLSisconsistent.Butweknowthatheteroskedasticitycausesstatisticalinferencebasedonthe
usualtandFstatisticstobeinvalid,eveninlargesamples.Asheteroskedasticityisaviolationof
theGaussMarkovassumptions,OLSisnolongerBLUE.
Q2.Wooldridge8.2
WithVar(u|inc,price,educ,female)=2inc2,h(x)=inc2,whereh(x)istheheteroskedasticity
functiondefinedinequation(8.21).Therefore,h(x)=inc,andsothetransformedequationis
obtainedbydividingtheoriginalequationbyinc:
beer/inc=0(1/inc)+1+2price/inc+3educ/inc+4female/inc+u/inc
Noticethat1,whichistheslopeonincintheoriginalmodel,isnowaconstantinthe
transformedequation.Thisissimplyaconsequenceoftheformoftheheteroskedasticityand
thefunctionalformsoftheexplanatoryvariablesintheoriginalequation.
Q3.Wooldridge8.3
False.TheunbiasednessofWLSandOLShingescruciallyonAssumptionMLR.4,and,asweknow
fromChapter4,thisassumptionisoftenviolatedwhenanimportantvariableisomitted.When
MLR.4doesnothold,bothWLSandOLSarebiased.Withoutspecificinformationonhowthe
omittedvariableiscorrelatedwiththeincludedexplanatoryvariables,itisnotpossibleto
determinewhichestimatorhasasmallbias.ItispossiblethatWLSwouldhavemorebiasthan
OLSorlessbias.Becausewecannotknow,weshouldnotclaimtouseWLSinordertosolve
biasesassociatedwithOLS.
Q4.Wooldridge8.5
(i)No.Foreachcoefficient,theusualstandarderrorsandtheheteroskedasticityrobustonesare
practicallyverysimilar.
(ii)Theeffectis.029(4)=.116,sotheprobabilityofsmokingfallsbyabout.116.
(iii)Asusual,wecomputetheturningpointinthequadratic:.020/[2(.00026)]38.46,soabout
38andonehalfyears.
(iv)Holdingotherfactorsintheequationfixed,apersoninastatewithrestaurantsmoking
restrictionshasa.101lowerchanceofsmoking.Thisissimilartotheeffectofhavingfourmore
yearsofeducation.
(v)WejustplugthevaluesoftheindependentvariablesintotheOLSregressionline:
=.656.069log(67.44)+.012log(6,500).029(16)+.020(77).00026(77).0052.
Thus,theestimatedprobabilityofsmokingforthispersonisclosetozero.(Infact,thispersonis
notasmoker,sotheequationpredictswellforthisparticularobservation.)
Q5.WooldridgeC8.10(401ksubs_c8_10.do)
(i)Inthefollowingequation,estimatedbyOLS,theusualstandarderrorsarein( ) and the
heteroskedasticityrobust standard errors are in [ ]:
401 =.506+.0124inc.000062inc2+.0265age.00031age2.0035male
(.081)(.0006)
(.000005)
(.0039) (.00005) (.0121)
[.079][.0006] [.000005]
[.0038][.00004] [.0121]
2
n=9,275,R =.094.
Therearenoimportantdifferences;ifanything,therobuststandarderrorsaresmaller.
(ii)Thisisageneralclaim.SinceVar(y|x)=p(x)[1p(x)],wecanwriteE(u2|x)=p(x)p(x)2.Written
inerrorform,u2=p(x)p(x)2+v.Inotherwords,wecanwritethisasaregressionmodelu2=0
+1p(x)+2p(x)2+v,withtherestrictions0=0,1=1,and2=1.Rememberthat,forthe
LPM,thefittedvalues, ,areestimatesofp(x).So,whenweruntheregression on and
(includinganintercept),theinterceptestimatesshouldbeclosetozero,thecoefficienton
shouldbeclosetoone,andthecoefficienton shouldbecloseto1.
(iii)TheWhiteLMstatisticandFstatisticabout581.9and310.32respectively,bothofwhichare
verysignificant.Thecoefficienton 401 isabout1.010,thecoefficienton 401 2about.970,
andtheinterceptisabout.009.Theseestimatesarequiteclosetowhatweexpecttofindfrom
thetheoryinpart(ii).
(iv)Thesmallestfittedvalueisabout.030andthelargestisabout.697.TheWLSestimatesof
theLPMare
401 =.488+.0126inc.000062inc2+.0255age.00030age2.0055male
(.076)(.0005)
(.000004)
(.0037)(.00004) (.0117)
2
n=9,275,R =.108.
TherearenoimportantdifferenceswiththeOLSestimates.Thelargestrelativechangeisinthe
coefficientonmale,butthisvariableisveryinsignificantusingeitherestimationmethod.
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more.
Course Hero has millions of course specific materials providing students with the best way to expand
their education.
Below is a small sample set of documents:
Below is a small sample set of documents:
UNSW - ECON - 2206
Week10TutorialExercisesReadingsReadChapter9thoroughly.MakesurethatyouknowthemeaningsoftheKeyTermsatthechapterend.ReviewQuestions(thesemayormaynotbediscussedintutorialclasses)Whatisfunctionalformmisspecification?Whatisitsmajorconsequenceinregressionan
UNSW - ECON - 2206
Week11TutorialExercisesReviewQuestions(thesemayormaynotbediscussedintutorialclasses)Whatarethemainfeaturesoftimeseriesdata?Howdotimeseriesdatadifferfromcrosssectionaldata?Themainfeaturesincludethefollowing.First,timeseriesdatahaveatemporalordering,whi
UNSW - ECON - ECON2101
12/10/2011Monopolyandown-price elasticity of demand curves. To understand why a monopoly always selects anoutput level for which market demand is ownprice elastic, one first needs to understand howthe own-price elasticity varies along a demandcurve
UNSW - ECON - ECON2101
ECON2101: Microeconomics 2 Prerequisites: You must have passed Micro 1 QMA or equivalent is strongly recommendedECON2101: Microeconomics 2Session 2, 2011 Make sure that you have access to thecourse on BlackboardLecturer: Paul Pezanis-ChristouTuto
UNSW - ECON - ECON2101
7/27/2010Preferences and UtilityChapter 3: preferencesChapter 4: utilityPreferences We want to find patterns/regularities in thebehaviour of decision-makers. This requires that there is some regularityor predictability in their behaviour. For con
UNSW - ECON - ECON2101
Rational Choice The principal behavioral postulate isthat a decisionmaker chooses itsmost preferred alternative from thoseavailable to it.ChoiceCh 5: ChoiceCh 6: Demand (sec. 2,5,6,8 & appendix)Ch 15: Market Demand (sec. 1& 2) In terms of our mod
UNSW - ECON - ECON2101
Consumers Surplus A consumers demand curve for a good(say good 1) tells us that, at any givenprice p1, the consumer is willing to buysome determinate quantity x1 of the good. When the consumer does so, he ispresumably better off than he was when he
UNSW - ECON - ECON2101
Thinking about uncertainty An individual makes a choice beforeknowing the state of the world in whichthe outcome will occur. The state of the world will determine thepayoff from the act. Example:Choice under uncertaintyChapter 12 choicetake my um
UNSW - ECON - ECON2101
8/24/2010Information in CompetitiveMarketsAsymmetric Information In purely competitive markets all agentsare fully informed about tradedcommodities and other aspects of themarket. What about markets for medical services,or insurance, or used cars
UNSW - ECON - ECON2101
4/18/2011TechnologyTechnologyChapter 18Production functionsIsoquantsMarginal ProductReturns to scaleTechnical rate of substitutionLong run vs Short run1Technology A technology decribes how inputs are convertedto some output Lecture is being
UNSW - ECON - ECON2101
9/16/2011Cost MinimizationCost minimisation& cost curvesChapters 20 and 21A firm is a cost-minimiser if it produces any givenoutput level y 0 at smallest possible total cost.c(y) denotes the firms smallest possible totalcost for producing y units
UNSW - ECON - ECON2101
26/09/2011Firm SupplyFirm Supply& Industry SupplyChapters 22 and 23Q: How does a firm decide how much product tosupply?A: This depends upon the firms technology (production function) market environment (prices) competitors behaviors ( competitio
UNSW - ECON - ECON2101
4/10/2011Pure MonopolyMonopolyA monopolized market has a single seller.The monopolists demand curve is the(downward sloping) market demand curve.So the monopolist can alter the market price byadjusting its output level.Chapter 24 (Sections 1 to 5)
UNSW - ECON - ECON2101
OligopolyFinally the last week! A monopoly is an industry consisting asingle firm. A duopoly is an industry consisting of twofirms. An oligopoly is an industry consisting of afew firms. Particularly, each firms ownprice or output decisions affect
UNSW - ECON - ECON2101
Chapter 37NAMEAsymmetric InformationIntroduction. The economics of information and incentives is a relatively new branch of microeconomics, in which much intriguing work isgoing on. This chapter shows you a sample of these problems and theway that ec
UNSW - ECON - ECON2101
Chapter 2NAMEBudget ConstraintIntroduction. These workouts are designed to build your skills in describing economic situations with graphs and algebra. Budget sets are agood place to start, because both the algebra and the graphing are veryeasy. Wher
UNSW - ECON - ECON2101
Chapter 3NAMEPreferencesIntroduction. In the previous section you learned how to use graphs toshow the set of commodity bundles that a consumer can aord. In thissection, you learn to put information about the consumers preferences onthe same kind of
UNSW - ECON - ECON2101
Chapter 5NAMEChoiceIntroduction. You have studied budgets, and you have studied prefer-ences. Now is the time to put these two ideas together and do somethingwith them. In this chapter you study the commodity bundle chosen by autility-maximizing con
UNSW - ECON - ECON2101
Chapter 12NAMEUncertaintyIntroduction. In Chapter 11, you learned some tricks that allow you touse techniques you already know for studying intertemporal choice. Hereyou will learn some similar tricks, so that you can use the same methodsto study ri
UNSW - ECON - ECON2101
Chapter 14NAMEConsumers SurplusIntroduction. In this chapter you will study ways to measure a con-sumers valuation of a good given the consumers demand curve for it.The basic logic is as follows: The height of the demand curve measureshow much the c
UNSW - ECON - ECON2101
Chapter 16NAMEEquilibriumIntroduction. Supply and demand problems are bread and butter foreconomists. In the problems below, you will typically want to solve forequilibrium prices and quantities by writing an equation that sets supplyequal to demand
UNSW - ECON - ECON2101
Chapter 18NAMETechnologyIntroduction. In this chapter you work with production functions, re-lating output of a rm to the inputs it uses. This theory will look familiarto you, because it closely parallels the theory of utility functions. In utilityt
UNSW - ECON - ECON2101
Chapter 20NAMECost MinimizationIntroduction. In the chapter on consumer choice, you studied a con-sumer who tries to maximize his utility subject to the constraint that hehas a xed amount of money to spend. In this chapter you study thebehavior of a
UNSW - ECON - ECON2101
Chapter 24NAMEMonopolyIntroduction. The prot-maximizing output of a monopolist is found bysolving for the output at which marginal revenue is equal to marginal cost.Having solved for this output, you nd the monopolists price by pluggingthe prot-maxi
UNSW - ECON - ECON2101
Chapter 27NAMEOligopolyIntroduction. In this chapter you will solve problems for rm and indus-try outcomes when the rms engage in Cournot competition, Stackelbergcompetition, and other sorts of oligopoly behavior. In Cournot competition, each rm choo
Uni. Westminster - BUSINESS - 203
CHAPTER 5STRATEGIC MANAGEMENT IN THE MULTINATIONALCOMPANY: CONTENT AND FORMULATIONLearning ObjectivesDefine the generic strategies of differentiation and low costUnderstand how low-cost and differentiation strategists make moneyRecall multinational
Berkeley - COMPSCI - 70
CS 70Discrete Mathematics and Probability TheoryFall 2011RaoRough Outline Lecture 1The reader shold be aware that thes notes have been subject to minimal if any editing and should notbe distributed.This course is about formal analysis in the contex
Berkeley - COMPSCI - 70
Midterm 1 is early.Before drop date.Before most other midterms in CS.CS70: Lecture 2. Outline.1. Propositions.2. Propositional Forms.3. Implication Again.4. Wasons Experiment5. Truth Tables6. QuantiersPropositions: Statements that are true or f
Berkeley - COMPSCI - 70
AdministrationMidterm 1 is not early after all.We dont think 3 weeks is enough material to merit a midterm.CS70: Lecture 3. Outline.1. Proofs2. Simple3. Direct4. by Contrapositive5. by Cases6. by ContradictionSimple theorem.Theorem: P = (P Q ).
Berkeley - COMPSCI - 70
AdministrationPiazza class formsI have some and will hand out with the following priority.1. Richard Dores Tuesday section.2. Attended section and account forms ran out.3. Everyone else.I am having more printed.and will advise on piazza about.1. ho
Berkeley - COMPSCI - 70
AdministrationAccount forms.I have some and will hand out after class.Sign in to class account and register, or we cant recordyour grades!CS70: Lecture 5. Outline.1. Graphs.2. Tilings. Prove a stronger theorem.3. Strong Induction.4. Well Ordering
Berkeley - COMPSCI - 70
AdministrationAccount forms.I have some and will hand out after class.Sign in to class account and register, or else!CS70: Lecture 6. Outline.1. Induction/Recursion2. Stable Marriage Problem (Notes 4.)Tournament has a cycle of length 3 if at all.T
Berkeley - COMPSCI - 70
AdministrationCS70: Satish Rao: Lecture 7. Outline.1. What day is it?2. Modular Arithmetic.3. Division4. Euclids Algorithm.5. Euclids Extended Algorithm.What day is it?What day (of the week) will it be on September 12, 2012?What day is it?What d
Berkeley - COMPSCI - 70
AdministrationPlease, be considerate in that last minute!AdministrationPlease, be considerate in that last minute!Thanks!CS70: Satish Rao: Lecture 8. Outline.1. Modular Arithmetic: review.2. Inverses for Modular Arithmetc: Greatest Common Divisor.
Berkeley - COMPSCI - 70
AdministrationMidterms1. Midterm 1. Wednesday. October 5. 8-10 PM. Dwinelle 155.2. Midterm 2. Wednesday. November 9. 8-10 PM. Dwinelle155.CS70: Satish Rao: Lecture 9. Outline.1. Extended GCD.2. Cryptography3. Public Key Cryptography4. RSA system
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 10. Outline.1. Cryptography2. Public Key Cryptography3. RSA system3.1 Efciency: Repeated Squaring.3.2 Correctness: Fermats Theorem.3.3 Construction.4. Warnings.Cryptography .AliceEveBobCryptography .Secret sAliceEv
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 11. Outline.1. Signature Schemes.2. Fermats Theorem: again.3. Secret Sharing.4. Polynomials.RSA reminderRSA reminderConstruction:Primes p, qRSA reminderConstruction:Primes p, qFind e with gcd(e, (p 1)(q 1) = 1Find d
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 12. Outline.1. Polynomials2. Secret Sharing3. Polynomial Interpolation4. Finite Fields5. Erasure CodingSecret Sharing.Secret Sharing.Share secret among k people.Secret Sharing.Share secret among k people.Secrecy: Any k
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 13. Outline.1. Error Correction.2. Brute Force3. Berlekamp-Welsh!Erasure Codes.SatelliteGPS deviceErasure Codes.SatelliteGPS device3 packet message.Erasure Codes.Satellite3 packet message.Lose 3 out 6 packets.GPS d
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 14. Outline.1. Error Correction.2. Brute Force3. Berlekamp-Welsh!Erasure Codes.SatelliteGPS deviceErasure Codes.SatelliteGPS device3 packet message.Erasure Codes.Satellite3 packet message.Lose 3 out 6 packets.GPS d
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 15. Outline.1. Counting.2. Enumerate3. Tree4. Rules of Counting5. Sample with/without replacement where orderdoes/doesnt matter.Probability is soon.but rst lets count.Count?How many outcomes possible for k coin tosses?H
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 16.1. Review.2. Stars/Bars.3. Balls in Bins.4. Combinatorial Proofs.5. Inclusion/ExclusionFirst rule: n1 n2 n3 . Product Rule.First rule: n1 n2 n3 . Product Rule.k Samples with replacement from n items: nk .First rule: n
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.1. Midterm Review: October 5, 8-10 PM, 155 Dwinelle.2. Midterm Exam: October 10, 7-9, 155 Dwinelle.3. Watch Piazza for extra ofce hours.Sunday hours!4. No class on October 10. Study. Ofce hours.CS70: Satish Rao: Lect
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.1. Midterm Exam: October 10, 7-9, 155 Dwinelle.2. Watch Piazza for extra ofce hours.Sunday: October 9th, 1-3 PM, 310 Soda. (Cook/Chan)Monday: October 10th. 10-11:30 AM, 687 Soda, Rao.Sunday: 3-6 PM, 310 Soda?3. No cl
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.1. Midterm grades on glookup.2. Curve according to department guidelines. (Googlegrading policy berkeley.)3. Extra credit really extra. (Set curve then add extra credit.)4. Solutions. Working on them.5. Some dont hav
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.1. Midterm regrades requests due by your section.CS70: Satish Rao: Lecture 20.1. Product Rule.2. Product Rule and Probability spaces.3. Independence.4. Inclusion/Exclusion.Product RuleWhat is Pr [A B ]?Pr [A B ] =
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.1. Midterm regrades requests due by your section.CS70: Satish Rao: Lecture 21.1. PSA discussion.2. Checksums.3. Coupon Collectors Problem.Consequences of PSA Treatment.The most common are impotence and incontinence.
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.Midterm regrades requests taken until Friday after lecture.(Watch piazza!)Anyone lose a water bottle from midterm?CS70: Satish Rao: Lecture 22.1. Random Varianbles.2. Distributions.3. Expectation4. Linearity of Exp
Berkeley - COMPSCI - 70
CS70: Satish Rao: Administration.Midterm regrades requests taken until today after lecture.(Watch piazza!)CS70: Satish Rao: Lecture 23.1. Linearity of Expectation2. Applications3. Important Distributions and Expectations.Expectation.Expectation.D
Berkeley - COMPSCI - 70
CS70: Satish Rao: Lecture 24.1. Geometric Distribution.2. Coupon Collector: Expectation.3. Poisson Distribution.Geometric Distribution.Flip a coin with heads probability p until you get heads.Geometric Distribution.Flip a coin with heads probabilit
Berkeley - COMPSCI - 70
AdministrationI have regraded midterms. Come get them after class.CS70: Satish Rao: Lecture 25.1. Distribution Comparison2. Poisson Application3. VarianceDistributions: continued.i.2 =4.1Pr [X = i ] = (e4 ) 4!iE [X ] = 400 1 2 3 4 5 6 7 8 9
Berkeley - COMPSCI - 70
AdministrationI have regraded midterms. Come get them after class.CS70: Satish Rao: Lecture 26.1. Variance Calculations.2. Independent random variables.3. Variance Properties.ExamplePr [X = 1] = .99Pr [X = 99] = .01ExamplePr [X = 1] = .99Pr [X
Berkeley - COMPSCI - 70
AdministrationI have regraded midterms. Come get them after class.CS70: Satish Rao: Lecture 27.1. Chebyshevs inequality. (Markovs inequality.2. Polling.Variance!What is it good for?!?Absolutely. something!What do we want to bound?Recall the numbe
Berkeley - COMPSCI - 70
AdministrationI have regraded midterms. Come get them after class.Joke.Joke.Why do computer scientists confuse Halloween and Christmas?Joke.Why do computer scientists confuse Halloween and Christmas?OCT 31 = DEC 25.Joke.Why do computer scientists
Berkeley - COMPSCI - 70
Administration1. Midterm 2, Wednesday, November 9th. 8-10PM, 155Dwinelle.2. HKN Midterm Review, Sunday, November 6th, 2-4, 306Soda.(Can someone conrm?)3. Midterm Review, Sunday, November 6th. 5-7PM, 155Dwinelle.4. Extra ofce hours: Tuesday, Novemb
Berkeley - COMPSCI - 70
Administration1. Midterm 2, Wednesday, November 9th. 8-10PM, 155Dwinelle.2. Extra ofce hours: Tuesday, November 8th, 4-9PM, 310Soda.3. No class on Wednesday!CS70: Satish Rao: Lecture 30.1. Review Joint distribution, conditional expectation.2. Cond
Berkeley - COMPSCI - 70
AdministrationHomework 11 Due Tuesday at 5:00 PMCS70: Satish Rao: Lecture 31.Continuous Probability1. Motivation.2. Continuous Random Variables.3. Cumulative Distribution Function.4. Probability Density Function5. Expectation and VarianceJames Bo
Berkeley - COMPSCI - 70
AdministrationCS70: Satish Rao: Lecture 31.Continuous Probability1. Review Continuous Probability.2. Exponential Distribution.3. Normal (Gaussian) Distribution.4. Central Limit Theorem.Continuous Probability is continuous space.Continuous Probabi
Berkeley - COMPSCI - 70
AdministrationMidterm regrades due by November 28th after lecture.CS70: Satish Rao: Lecture 33.Continuous Probability1. Normal (Gaussian) Distribution.2. Joint distributions.3. Buffons needle.4. Begin inference.Normal Distribution.For any and , a
Berkeley - COMPSCI - 70
AdministrationMidterm regrades due by November 28th after lecture.CS70: Satish Rao: Lecture 33.Continuous Probability1. Normal (Gaussian) Distribution.2. Joint distributions.3. Buffons needle.4. Begin inference.Normal Distribution.For any and , a